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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering & Structured Output	20%	- Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment - JSON schema design and structured output enforcement
Context Management & Reliability	15%	- Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization
Agentic Architecture & Orchestration	27%	- Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling
Tool Design & MCP Integration	18%	- Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - MCP tool, resource and prompt implementation
Claude Code Configuration & Workflows	20%	- Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q78-Q83):

NEW QUESTION # 78
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering-helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?

A. Add detailed explanations to the project's CLAUDE.md describing which patterns are intentional, that linting is handled separately by CI, and that the src/gen/ directory contains automatically generated template code.
B. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
C. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and apply stricter evidence requirements to rendering-related issues.

Answer: B

Explanation:
Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses.
Option B configures a self-hosted GitHub Actions workflow, but the scenario concerns the separate managed Code Review service running on Anthropic's infrastructure. Instructions in that workflow do not control the managed reviewer. Option C provides useful general project context, but CLAUDE.md has lower review- specific authority: managed Code Review treats violations of it primarily as nit-level findings. REVIEW.md is the stronger and more precise mechanism for changing what the managed service reports. The file should skip src/gen/**, suppress style issues already enforced by CI, and require concrete behavioral evidence for rendering-helper warnings while continuing to report verified correctness and security defects.

NEW QUESTION # 79
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?

A. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it for areas where generated tests consistently require substantial editing.
B. Document testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended use cases, and examples distinguishing meaningful behavioral tests from trivial assertions.
C. Add post-generation coverage analysis that automatically filters out any generated test that does not increase line coverage beyond existing tests.
D. Implement two-phase generation in which a second Claude call scores each test against quality criteria, filtering out low-scoring tests before presenting results to developers.

Answer: B

Explanation:
Option D improves the generation conditions instead of filtering defective output afterward.
Anthropic recommends using CLAUDE.md for repository-wide standards and information that Claude should apply in every session. The file can define what constitutes a useful test, require assertions on observable behavior, identify approved fixture factories, prohibit duplication of existing scenarios, and provide contrasting examples of strong and trivial tests. These instructions are available before Claude decides what tests to create.

NEW QUESTION # 80
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A. Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
B. Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
C. Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
D. Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis

Answer: D

Explanation:
Parallelizing independent precedent analyses reduces latency, while keeping delegation at the coordinator level preserves centralized visibility, tracing, and debugging. Recursive delegation would make monitoring more difficult.

NEW QUESTION # 81
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

A. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.
B. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
C. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
D. Continue in the current context with more targeted prompts referencing the specific classes by name.

Answer: C

Explanation:
The current context is showing degradation and losing project-specific details. A focused summary preserves the relevant VulkanPipeline and FrameGraph findings, while a fresh sub- agent provides clean context for exploring physics and relating it to rendering.

NEW QUESTION # 82
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?

A. Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.
B. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
C. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and impose stricter evidence requirements on rendering-related issues.
D. Add detailed explanations to the project's CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.

Answer: B

Explanation:
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.
Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic's infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.

NEW QUESTION # 83
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